dsRNA-induced condensation of antiviral proteins modulates PKR activity

Significance The presence of dsRNA in the cytosol is a marker of infection and elicits an immune response. One aspect of this immune response is the activation of the eIF2α kinase PKR, which results in translational reprogramming and stress granule formation. Here, we show that dsRNA induces the formation of a novel condensate by PKR that is distinct from other known ribonucleoprotein assemblies. These results challenge prior observations that PKR is recruited to stress granules and suggest that the condensation of PKR may be a mechanism that cells use to modulate PKR activation.

Fluorescent cells were also selected for using fluorescence activated cell sorting (FACS).

Immunofluorescence
Immunofluorescence was performed as described in (7). The following antibodies were used at 1:500

Sequential Immunofluorescence and FISH
Sequential immunofluorescence and FISH was performed as described in (8). Poly d(T) probes were ordered from Integrated DNA Technologies as 30 deoxythymidines with a 5' Cy5 modification.

SDS-PAGE and Western Blotting
SDS-PAGE and Western blotting was performed as described in (7). The following antibodies were used dRIF enrichment was calculated using ImageJ. Maximum intensity projections of all z slices were generated, a line was drawn through the dRIF and through the cytoplasm, and the average intensity of each line was calculated. Enrichment was calculated as dRIF intensity/cytoplasm intensity. dRIF and cytoplasm volume were calculated using Bitplane Imaris software as described in (7,9) for stress granule quantification. Statistical analysis was performed using an unpaired two-tailed t-test. For p > 0.05, analyses were designated as not significant.
A custom MATLAB script was developed to analyze the resulting images. The script identified cells and both fluorescently labeled PKR and G3BP1 puncta, tracked individual PKR spots over time, and measured the distance between PKR and G3BP1 puncta. The datasets were initially preprocessed to calculate the maximum intensity projection (MIP) for each image.
To identify individual cells, the mApple and GFP MIP images were summed, then an intensity threshold was manually chosen to distinguish the fluorescent cells from the dark background. This process yielded a binary mask, where each pixel in the image that belonged to a cell was labeled true and every other pixel was labeled false. The watershed algorithm was then employed to separate touching cells. As the cells were occasionally confluent or had protrusions, the binary mask was manually edited using ImageJ/Fiji to correct mislabeled pixels as necessary.
To identify the puncta in the mApple (corresponding to dRIFs) and the GFP (corresponding to stress granules) channels, each MIP image was first blurred using a Gaussian filter with a standard deviation of 0.5 to smooth the image and remove any "hot" pixels. The background signal within each cell was then subtracted by applying a morphological top hat filter, with a 2-pixel disk shaped structuring element. The final background subtracted image was then subjected to an intensity threshold to obtain a mask identifying each spot. Additionally, we used the edited cell masks to exclude any spots that were found outside of the cells of interest.
Finally, a previously developed tracking algorithm (10) was then used to track the identified PKR puncta over the course of the movie. For each frame in which both PKR and G3BP1 puncta existed, the algorithm also computed the straight-line distance between the PKR and GFP spots. The resulting data was then analyzed to generate the movie files, static images, and histograms, as shown in the results. The code used to analyze the images in this publication is available for download at https://github.com/jwtay1/tracking-stress-granules/.
FRAP A549 cells were grown in 24-well glass plates (Cellvis #P24-1.5H-N). Cell media was replaced with OptiMEM media immediately prior to imaging. FRAP was done on a Nikon A1R Laser Scanning Confocal microscope as follows: acquire 2 images 5 second apart prior to bleaching, bleach for 9.54 seconds with 405 and 488 nm lasers at 100% laser power, image every 5 seconds second for 1 minute during recovery. Mean intensity within the bleached area was determined using Nikon Elements software. Intensities were normalized to the average intensity of the region of interest (ROI) at time 0 (set to 1) and at the first time-point post-bleaching (set to 0). Graphs represent averages of three independent experiments where at least 10 foci were bleached for each experiment. Error bars represent standard deviation.